Overview

Dataset statistics

Number of variables29
Number of observations49
Missing cells47
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.3 KiB
Average record size in memory257.7 B

Variable types

Numeric21
Text2
Categorical4
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000360

Alerts

SHIP_HGHT is highly imbalanced (85.6%)Imbalance
SHIP_OWNER_NM is highly imbalanced (66.8%)Imbalance
DRAFT is highly imbalanced (85.6%)Imbalance
SHIP_NM has 2 (4.1%) missing valuesMissing
SHPYRD_NM has 45 (91.8%) missing valuesMissing
MMSI has unique valuesUnique
ARVL_HMS has unique valuesUnique
AVE_VE has unique valuesUnique
MAX_VE has unique valuesUnique
NVGTN_DIST has unique valuesUnique
AVE_WDSP has unique valuesUnique
LD_RT has unique valuesUnique
RN has unique valuesUnique
IMO_IDNTF_NO has 19 (38.8%) zerosZeros
SHIP_WDTH has 20 (40.8%) zerosZeros
SHIP_LNTH has 20 (40.8%) zerosZeros
BULD_YR has 19 (38.8%) zerosZeros
DDWGHT has 19 (38.8%) zerosZeros
DPTRP_LA has 4 (8.2%) zerosZeros
DPTRP_LO has 4 (8.2%) zerosZeros
DTNT_LA has 15 (30.6%) zerosZeros
DTNT_LO has 15 (30.6%) zerosZeros
WAVE_MAX_CYCL has 6 (12.2%) zerosZeros
WAVE_AVE_CYCL has 6 (12.2%) zerosZeros
WAVE_MAX_HGHT has 6 (12.2%) zerosZeros
WAVE_AVE_HGHT has 6 (12.2%) zerosZeros
MAX_WDSP has 1 (2.0%) zerosZeros
AVE_WDSP has 1 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:32:09.573098
Analysis finished2023-12-10 14:32:09.855054
Duration0.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MMSI
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0728277 × 108
Minimum2.05681 × 108
Maximum2.1151442 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:09.928440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.05681 × 108
5-th percentile2.05688 × 108
Q12.05736 × 108
median2.05772 × 108
Q32.0726121 × 108
95-th percentile2.1151394 × 108
Maximum2.1151442 × 108
Range5833420
Interquartile range (IQR)1525208

Descriptive statistics

Standard deviation2237157.7
Coefficient of variation (CV)0.010792782
Kurtosis-0.089049493
Mean2.0728277 × 108
Median Absolute Deviation (MAD)87000
Skewness1.2612458
Sum1.0156856 × 1010
Variance5.0048746 × 1012
MonotonicityNot monotonic
2023-12-10T23:32:10.073531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
205681000 1
 
2.0%
211513350 1
 
2.0%
207072333 1
 
2.0%
207072335 1
 
2.0%
207072343 1
 
2.0%
207072385 1
 
2.0%
207072425 1
 
2.0%
207072429 1
 
2.0%
211463570 1
 
2.0%
211464390 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
205681000 1
2.0%
205685000 1
2.0%
205686000 1
2.0%
205691000 1
2.0%
205692000 1
2.0%
205700111 1
2.0%
205703000 1
2.0%
205706000 1
2.0%
205709000 1
2.0%
205710000 1
2.0%
ValueCountFrequency (%)
211514420 1
2.0%
211514340 1
2.0%
211514040 1
2.0%
211513790 1
2.0%
211513570 1
2.0%
211513350 1
2.0%
211468560 1
2.0%
211464830 1
2.0%
211464390 1
2.0%
211463570 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6536498.8
Minimum0
Maximum47000004
Zeros19
Zeros (%)38.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:10.208017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9302994
Q39719276
95-th percentile9743045.2
Maximum47000004
Range47000004
Interquartile range (IQR)9719276

Descriptive statistics

Standard deviation7502330.2
Coefficient of variation (CV)1.1477597
Kurtosis17.056467
Mean6536498.8
Median Absolute Deviation (MAD)429583
Skewness3.1744108
Sum3.2028844 × 108
Variance5.6284958 × 1013
MonotonicityNot monotonic
2023-12-10T23:32:10.325798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 19
38.8%
9659139 1
 
2.0%
9687497 1
 
2.0%
47000004 1
 
2.0%
9010931 1
 
2.0%
6817857 1
 
2.0%
7911052 1
 
2.0%
9336971 1
 
2.0%
9474541 1
 
2.0%
9448877 1
 
2.0%
Other values (21) 21
42.9%
ValueCountFrequency (%)
0 19
38.8%
6817857 1
 
2.0%
7911052 1
 
2.0%
9010931 1
 
2.0%
9246633 1
 
2.0%
9255763 1
 
2.0%
9302994 1
 
2.0%
9336971 1
 
2.0%
9336983 1
 
2.0%
9442184 1
 
2.0%
ValueCountFrequency (%)
47000004 1
2.0%
9789324 1
2.0%
9750024 1
2.0%
9732577 1
2.0%
9732565 1
2.0%
9732553 1
2.0%
9732541 1
2.0%
9728708 1
2.0%
9722924 1
2.0%
9719305 1
2.0%

SHIP_NM
Text

MISSING 

Distinct47
Distinct (%)100.0%
Missing2
Missing (%)4.1%
Memory size524.0 B
2023-12-10T23:32:10.555598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length8.4255319
Min length4

Characters and Unicode

Total characters396
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)100.0%

Sample

1st rowWARISOULX
2nd rowKNOKKE
3rd rowALICE
4th rowKONTICH
5th rowALEX
ValueCountFrequency (%)
deymann 2
 
3.1%
gener8 2
 
3.1%
warisoulx 1
 
1.5%
d 1
 
1.5%
rigel 1
 
1.5%
aries 1
 
1.5%
lira 1
 
1.5%
castor 1
 
1.5%
paceas 1
 
1.5%
regina 1
 
1.5%
Other values (53) 53
81.5%
2023-12-10T23:32:10.943974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 57
14.4%
E 48
12.1%
R 33
 
8.3%
N 31
 
7.8%
I 30
 
7.6%
O 22
 
5.6%
L 21
 
5.3%
G 19
 
4.8%
18
 
4.5%
K 17
 
4.3%
Other values (20) 100
25.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 374
94.4%
Space Separator 18
 
4.5%
Decimal Number 3
 
0.8%
Other Punctuation 1
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 57
15.2%
E 48
12.8%
R 33
 
8.8%
N 31
 
8.3%
I 30
 
8.0%
O 22
 
5.9%
L 21
 
5.6%
G 19
 
5.1%
K 17
 
4.5%
S 13
 
3.5%
Other values (16) 83
22.2%
Decimal Number
ValueCountFrequency (%)
8 2
66.7%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
18
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 374
94.4%
Common 22
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 57
15.2%
E 48
12.8%
R 33
 
8.8%
N 31
 
8.3%
I 30
 
8.0%
O 22
 
5.9%
L 21
 
5.6%
G 19
 
5.1%
K 17
 
4.5%
S 13
 
3.5%
Other values (16) 83
22.2%
Common
ValueCountFrequency (%)
18
81.8%
8 2
 
9.1%
. 1
 
4.5%
2 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 57
14.4%
E 48
12.1%
R 33
 
8.3%
N 31
 
7.8%
I 30
 
7.6%
O 22
 
5.6%
L 21
 
5.3%
G 19
 
4.8%
18
 
4.5%
K 17
 
4.3%
Other values (20) 100
25.3%

SHIP_KIND
Categorical

Distinct16
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
LPG Tanker
13 
Crude Oil Tanker
7 
Inland Motor Tanker liquid cargo type C
7 
OIL PRODUCTS TANKER
3 
Inland Motor Tanker
3 
Other values (11)
16 

Length

Max length45
Median length31
Mean length19.734694
Min length4

Unique

Unique7 ?
Unique (%)14.3%

Sample

1st rowLPG Tanker
2nd rowLPG Tanker
3rd rowCrude Oil Tanker
4th rowLPG Tanker
5th rowCrude Oil Tanker

Common Values

ValueCountFrequency (%)
LPG Tanker 13
26.5%
Crude Oil Tanker 7
14.3%
Inland Motor Tanker liquid cargo type C 7
14.3%
OIL PRODUCTS TANKER 3
 
6.1%
Inland Motor Tanker 3
 
6.1%
Inland Motor Tanker liquid cargo type N 3
 
6.1%
<NA> 2
 
4.1%
Oil Products Tanker 2
 
4.1%
Inland Tanker 2
 
4.1%
LNG Tanker 1
 
2.0%
Other values (6) 6
12.2%

Length

2023-12-10T23:32:11.092872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tanker 45
26.2%
inland 19
11.0%
lpg 13
 
7.6%
oil 13
 
7.6%
motor 13
 
7.6%
cargo 11
 
6.4%
liquid 10
 
5.8%
type 10
 
5.8%
crude 8
 
4.7%
c 7
 
4.1%
Other values (14) 23
13.4%

SHIP_WDTH
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.212449
Minimum0
Maximum34
Zeros20
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:11.221409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q311
95-th percentile19.8
Maximum34
Range34
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.1140831
Coefficient of variation (CV)1.1250108
Kurtosis1.8385205
Mean7.212449
Median Absolute Deviation (MAD)9
Skewness1.2207193
Sum353.41
Variance65.838344
MonotonicityNot monotonic
2023-12-10T23:32:11.394074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 20
40.8%
11.4 4
 
8.2%
9.5 4
 
8.2%
11.0 3
 
6.1%
16.5 3
 
6.1%
9.6 2
 
4.1%
22.0 1
 
2.0%
34.0 1
 
2.0%
14.0 1
 
2.0%
9.86 1
 
2.0%
Other values (9) 9
18.4%
ValueCountFrequency (%)
0.0 20
40.8%
4.13642e-38 1
 
2.0%
4.13971e-38 1
 
2.0%
5.0 1
 
2.0%
8.0 1
 
2.0%
9.0 1
 
2.0%
9.5 4
 
8.2%
9.6 2
 
4.1%
9.86 1
 
2.0%
10.0 1
 
2.0%
ValueCountFrequency (%)
34.0 1
 
2.0%
30.0 1
 
2.0%
22.0 1
 
2.0%
16.5 3
6.1%
16.0 1
 
2.0%
14.0 1
 
2.0%
11.4 4
8.2%
11.0 3
6.1%
10.25 1
 
2.0%
10.0 1
 
2.0%

SHIP_LNTH
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.062245
Minimum0
Maximum209
Zeros20
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:11.568201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median64.5
Q397.24
95-th percentile110.6
Maximum209
Range209
Interquartile range (IQR)97.24

Descriptive statistics

Standard deviation54.991299
Coefficient of variation (CV)0.99871153
Kurtosis-0.59095413
Mean55.062245
Median Absolute Deviation (MAD)46.5
Skewness0.43422945
Sum2698.05
Variance3024.0429
MonotonicityNot monotonic
2023-12-10T23:32:11.718586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 20
40.8%
110.0 6
 
12.2%
4.57244e-41 2
 
4.1%
97.23 2
 
4.1%
97.24 2
 
4.1%
80.0 2
 
4.1%
86.0 2
 
4.1%
165.0 1
 
2.0%
80.5 1
 
2.0%
95.3 1
 
2.0%
Other values (10) 10
20.4%
ValueCountFrequency (%)
0.0 20
40.8%
4.57244e-41 2
 
4.1%
47.7 1
 
2.0%
64.01 1
 
2.0%
64.5 1
 
2.0%
80.0 2
 
4.1%
80.5 1
 
2.0%
85.0 1
 
2.0%
86.0 2
 
4.1%
90.0 1
 
2.0%
ValueCountFrequency (%)
209.0 1
 
2.0%
165.0 1
 
2.0%
111.0 1
 
2.0%
110.0 6
12.2%
106.0 1
 
2.0%
100.1 1
 
2.0%
99.0 1
 
2.0%
97.24 2
 
4.1%
97.23 2
 
4.1%
95.3 1
 
2.0%

SHIP_HGHT
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
18.2
 
1

Length

Max length4
Median length3
Mean length3.0204082
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row18.2
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
18.2 1
 
2.0%

Length

2023-12-10T23:32:11.882929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:11.986499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
18.2 1
 
2.0%

SHIP_OWNER_NM
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
<NA>
46 
GENERAL MARITIME MANAGEMENT
 
3

Length

Max length27
Median length4
Mean length5.4081633
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 46
93.9%
GENERAL MARITIME MANAGEMENT 3
 
6.1%

Length

2023-12-10T23:32:12.081800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:12.172612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 46
83.6%
general 3
 
5.5%
maritime 3
 
5.5%
management 3
 
5.5%

DRAFT
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
48 
102
 
1

Length

Max length3
Median length1
Mean length1.0408163
Min length1

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row102
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48
98.0%
102 1
 
2.0%

Length

2023-12-10T23:32:12.295331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:12.417326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 48
98.0%
102 1
 
2.0%

SHPYRD_NM
Text

MISSING 

Distinct2
Distinct (%)50.0%
Missing45
Missing (%)91.8%
Memory size524.0 B
2023-12-10T23:32:12.531484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length26
Mean length32.5
Min length26

Characters and Unicode

Total characters130
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)25.0%

Sample

1st rowDAEWOO SHIPBUILDING & MARINE ENGINEERING - GEOJE, KR
2nd rowJMU TSU SHIPYARD - TSU, JP
3rd rowJMU TSU SHIPYARD - TSU, JP
4th rowJMU TSU SHIPYARD - TSU, JP
ValueCountFrequency (%)
tsu 6
23.1%
5
19.2%
jmu 3
11.5%
shipyard 3
11.5%
jp 3
11.5%
daewoo 1
 
3.8%
shipbuilding 1
 
3.8%
marine 1
 
3.8%
engineering 1
 
3.8%
geoje 1
 
3.8%
2023-12-10T23:32:12.823498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
16.9%
U 10
 
7.7%
S 10
 
7.7%
I 9
 
6.9%
J 7
 
5.4%
P 7
 
5.4%
E 7
 
5.4%
R 6
 
4.6%
T 6
 
4.6%
N 5
 
3.8%
Other values (14) 41
31.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 99
76.2%
Space Separator 22
 
16.9%
Other Punctuation 5
 
3.8%
Dash Punctuation 4
 
3.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 10
 
10.1%
S 10
 
10.1%
I 9
 
9.1%
J 7
 
7.1%
P 7
 
7.1%
E 7
 
7.1%
R 6
 
6.1%
T 6
 
6.1%
N 5
 
5.1%
D 5
 
5.1%
Other values (10) 27
27.3%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
& 1
 
20.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 99
76.2%
Common 31
 
23.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 10
 
10.1%
S 10
 
10.1%
I 9
 
9.1%
J 7
 
7.1%
P 7
 
7.1%
E 7
 
7.1%
R 6
 
6.1%
T 6
 
6.1%
N 5
 
5.1%
D 5
 
5.1%
Other values (10) 27
27.3%
Common
ValueCountFrequency (%)
22
71.0%
- 4
 
12.9%
, 4
 
12.9%
& 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
16.9%
U 10
 
7.7%
S 10
 
7.7%
I 9
 
6.9%
J 7
 
5.4%
P 7
 
5.4%
E 7
 
5.4%
R 6
 
4.6%
T 6
 
4.6%
N 5
 
3.8%
Other values (14) 41
31.5%

BULD_YR
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1229.8367
Minimum0
Maximum2018
Zeros19
Zeros (%)38.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:12.974125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2003
Q32016
95-th percentile2017.6
Maximum2018
Range2018
Interquartile range (IQR)2016

Descriptive statistics

Standard deviation988.92299
Coefficient of variation (CV)0.80410917
Kurtosis-1.8511646
Mean1229.8367
Median Absolute Deviation (MAD)15
Skewness-0.4751075
Sum60262
Variance977968.68
MonotonicityNot monotonic
2023-12-10T23:32:13.095837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 19
38.8%
2016 8
16.3%
2017 5
 
10.2%
2009 4
 
8.2%
2018 3
 
6.1%
2007 2
 
4.1%
2015 1
 
2.0%
2003 1
 
2.0%
2008 1
 
2.0%
1980 1
 
2.0%
Other values (4) 4
 
8.2%
ValueCountFrequency (%)
0 19
38.8%
1968 1
 
2.0%
1980 1
 
2.0%
1986 1
 
2.0%
1992 1
 
2.0%
1993 1
 
2.0%
2003 1
 
2.0%
2007 2
 
4.1%
2008 1
 
2.0%
2009 4
 
8.2%
ValueCountFrequency (%)
2018 3
 
6.1%
2017 5
10.2%
2016 8
16.3%
2015 1
 
2.0%
2009 4
8.2%
2008 1
 
2.0%
2007 2
 
4.1%
2003 1
 
2.0%
1993 1
 
2.0%
1992 1
 
2.0%

DDWGHT
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66875.796
Minimum0
Maximum441585
Zeros19
Zeros (%)38.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:13.232317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3121
Q329649
95-th percentile299196.4
Maximum441585
Range441585
Interquartile range (IQR)29649

Descriptive statistics

Standard deviation118771.28
Coefficient of variation (CV)1.775998
Kurtosis1.6382007
Mean66875.796
Median Absolute Deviation (MAD)3121
Skewness1.7257676
Sum3276914
Variance1.4106616 × 1010
MonotonicityNot monotonic
2023-12-10T23:32:13.379780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 19
38.8%
29588 2
 
4.1%
29649 2
 
4.1%
28590 1
 
2.0%
298991 1
 
2.0%
2477 1
 
2.0%
2698 1
 
2.0%
1061 1
 
2.0%
1655 1
 
2.0%
1063 1
 
2.0%
Other values (19) 19
38.8%
ValueCountFrequency (%)
0 19
38.8%
1061 1
 
2.0%
1063 1
 
2.0%
1655 1
 
2.0%
2477 1
 
2.0%
2698 1
 
2.0%
3121 1
 
2.0%
3990 1
 
2.0%
3996 1
 
2.0%
4000 1
 
2.0%
ValueCountFrequency (%)
441585 1
2.0%
299446 1
2.0%
299320 1
2.0%
299011 1
2.0%
299010 1
2.0%
298991 1
2.0%
298767 1
2.0%
298642 1
2.0%
150296 1
2.0%
149876 1
2.0%

DPTR_HMS
Date

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-01-01 00:00:01
Maximum2021-07-27 01:07:03
2023-12-10T23:32:13.487140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:13.600916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)

ARVL_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-03-11 05:56:03
Maximum2021-07-31 23:59:58
2023-12-10T23:32:13.722871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:13.859673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

DPTRP_LA
Real number (ℝ)

ZEROS 

Distinct46
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.327761
Minimum-25.943701
Maximum60.804798
Zeros4
Zeros (%)8.2%
Negative5
Negative (%)10.2%
Memory size573.0 B
2023-12-10T23:32:13.988002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-25.943701
5-th percentile-10.530852
Q118.874901
median43.193901
Q348.136002
95-th percentile52.143381
Maximum60.804798
Range86.748499
Interquartile range (IQR)29.261101

Descriptive statistics

Standard deviation22.768725
Coefficient of variation (CV)0.72679071
Kurtosis-0.12086995
Mean31.327761
Median Absolute Deviation (MAD)8.353001
Skewness-0.99190297
Sum1535.0603
Variance518.41486
MonotonicityNot monotonic
2023-12-10T23:32:14.104604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 4
 
8.2%
-3.58239 1
 
2.0%
51.546902 1
 
2.0%
44.2742 1
 
2.0%
43.857201 1
 
2.0%
43.857601 1
 
2.0%
44.332401 1
 
2.0%
44.23 1
 
2.0%
44.3232 1
 
2.0%
50.957901 1
 
2.0%
Other values (36) 36
73.5%
ValueCountFrequency (%)
-25.943701 1
 
2.0%
-23.1623 1
 
2.0%
-12.986 1
 
2.0%
-6.84813 1
 
2.0%
-3.58239 1
 
2.0%
0.0 4
8.2%
2.34608 1
 
2.0%
14.9983 1
 
2.0%
15.6967 1
 
2.0%
18.874901 1
 
2.0%
ValueCountFrequency (%)
60.804798 1
2.0%
56.948399 1
2.0%
52.194901 1
2.0%
52.066101 1
2.0%
51.8479 1
2.0%
51.546902 1
2.0%
51.467201 1
2.0%
51.339901 1
2.0%
51.240601 1
2.0%
50.957901 1
2.0%

DPTRP_LO
Real number (ℝ)

ZEROS 

Distinct46
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.595645
Minimum-115.376
Maximum121.956
Zeros4
Zeros (%)8.2%
Negative10
Negative (%)20.4%
Memory size573.0 B
2023-12-10T23:32:14.227809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-115.376
5-th percentile-96.701958
Q10
median13.726
Q328.0895
95-th percentile90.145482
Maximum121.956
Range237.332
Interquartile range (IQR)28.0895

Descriptive statistics

Standard deviation49.113686
Coefficient of variation (CV)3.8992593
Kurtosis1.7051665
Mean12.595645
Median Absolute Deviation (MAD)14.312099
Skewness-0.66231233
Sum617.1866
Variance2412.1541
MonotonicityNot monotonic
2023-12-10T23:32:14.353484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 4
 
8.2%
-81.683899 1
 
2.0%
7.12776 1
 
2.0%
28.0895 1
 
2.0%
25.9545 1
 
2.0%
25.9554 1
 
2.0%
28.6682 1
 
2.0%
22.6733 1
 
2.0%
28.038099 1
 
2.0%
6.99131 1
 
2.0%
Other values (36) 36
73.5%
ValueCountFrequency (%)
-115.375999 1
2.0%
-111.649002 1
2.0%
-106.713997 1
2.0%
-81.683899 1
2.0%
-75.4133 1
2.0%
-44.414001 1
2.0%
-8.61952 1
2.0%
-6.31088 1
2.0%
-1.90413 1
2.0%
-1.47899 1
2.0%
ValueCountFrequency (%)
121.956001 1
2.0%
112.500999 1
2.0%
101.911003 1
2.0%
72.4972 1
2.0%
68.789703 1
2.0%
66.756699 1
2.0%
61.888599 1
2.0%
58.702499 1
2.0%
29.7383 1
2.0%
29.2162 1
2.0%

DTNT_LA
Real number (ℝ)

ZEROS 

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.413395
Minimum-30.496
Maximum60.815399
Zeros15
Zeros (%)30.6%
Negative4
Negative (%)8.2%
Memory size573.0 B
2023-12-10T23:32:14.465620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-30.496
5-th percentile-5.80079
Q10
median27.712299
Q347.883499
95-th percentile52.845699
Maximum60.815399
Range91.311399
Interquartile range (IQR)47.883499

Descriptive statistics

Standard deviation24.599572
Coefficient of variation (CV)1.0506623
Kurtosis-1.4573554
Mean23.413395
Median Absolute Deviation (MAD)24.1801
Skewness-0.1008824
Sum1147.2563
Variance605.13894
MonotonicityNot monotonic
2023-12-10T23:32:14.573361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 15
30.6%
27.8305 1
 
2.0%
51.892399 1
 
2.0%
49.304001 1
 
2.0%
43.857101 1
 
2.0%
43.857601 1
 
2.0%
44.248299 1
 
2.0%
49.0718 1
 
2.0%
49.4893 1
 
2.0%
51.6465 1
 
2.0%
Other values (25) 25
51.0%
ValueCountFrequency (%)
-30.496 1
 
2.0%
-9.24461 1
 
2.0%
-6.41595 1
 
2.0%
-4.87805 1
 
2.0%
0.0 15
30.6%
2.34323 1
 
2.0%
4.56612 1
 
2.0%
6.345 1
 
2.0%
22.5998 1
 
2.0%
26.192101 1
 
2.0%
ValueCountFrequency (%)
60.815399 1
2.0%
59.2705 1
2.0%
53.2971 1
2.0%
52.168598 1
2.0%
51.892399 1
2.0%
51.6465 1
2.0%
51.444698 1
2.0%
51.3563 1
2.0%
50.987999 1
2.0%
49.4893 1
2.0%

DTNT_LO
Real number (ℝ)

ZEROS 

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.092267
Minimum-165.215
Maximum127.762
Zeros15
Zeros (%)30.6%
Negative5
Negative (%)10.2%
Memory size573.0 B
2023-12-10T23:32:14.677564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-165.215
5-th percentile-56.849362
Q10
median4.36764
Q325.9548
95-th percentile101.3632
Maximum127.762
Range292.977
Interquartile range (IQR)25.9548

Descriptive statistics

Standard deviation46.811342
Coefficient of variation (CV)4.2201782
Kurtosis4.9395593
Mean11.092267
Median Absolute Deviation (MAD)4.36764
Skewness-0.64251213
Sum543.52108
Variance2191.3018
MonotonicityNot monotonic
2023-12-10T23:32:15.021100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 15
30.6%
-97.497498 1
 
2.0%
4.36764 1
 
2.0%
-165.214996 1
 
2.0%
25.9545 1
 
2.0%
25.9554 1
 
2.0%
27.9 1
 
2.0%
8.33701 1
 
2.0%
8.44777 1
 
2.0%
6.60392 1
 
2.0%
Other values (25) 25
51.0%
ValueCountFrequency (%)
-165.214996 1
 
2.0%
-97.497498 1
 
2.0%
-83.683403 1
 
2.0%
-16.598301 1
 
2.0%
-10.2006 1
 
2.0%
0.0 15
30.6%
1.28043 1
 
2.0%
2.42667 1
 
2.0%
4.22207 1
 
2.0%
4.29433 1
 
2.0%
ValueCountFrequency (%)
127.762001 1
2.0%
119.411003 1
2.0%
101.919998 1
2.0%
100.528 1
2.0%
61.730301 1
2.0%
53.205601 1
2.0%
49.4655 1
2.0%
39.427101 1
2.0%
28.6602 1
2.0%
27.9139 1
2.0%

AVE_VE
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean830.85208
Minimum0.00238529
Maximum14272.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:15.136894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00238529
5-th percentile6.478962
Q112.2491
median20.3345
Q3150.597
95-th percentile6465.84
Maximum14272.3
Range14272.298
Interquartile range (IQR)138.3479

Descriptive statistics

Standard deviation2838.1414
Coefficient of variation (CV)3.4159407
Kurtosis14.647724
Mean830.85208
Median Absolute Deviation (MAD)9.6874
Skewness3.9176961
Sum40711.752
Variance8055046.6
MonotonicityNot monotonic
2023-12-10T23:32:15.256180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
14.8503 1
 
2.0%
20.5429 1
 
2.0%
258.902 1
 
2.0%
14272.3 1
 
2.0%
540.953 1
 
2.0%
15.7639 1
 
2.0%
736.12 1
 
2.0%
37.4524 1
 
2.0%
6.74123 1
 
2.0%
45.2073 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.00238529 1
2.0%
5.36919 1
2.0%
6.43163 1
2.0%
6.54996 1
2.0%
6.74123 1
2.0%
10.9295 1
2.0%
11.5218 1
2.0%
11.676 1
2.0%
11.8208 1
2.0%
11.9933 1
2.0%
ValueCountFrequency (%)
14272.3 1
2.0%
10807.4 1
2.0%
9802.16 1
2.0%
1461.36 1
2.0%
736.12 1
2.0%
554.038 1
2.0%
540.953 1
2.0%
492.117 1
2.0%
327.062 1
2.0%
282.821 1
2.0%

MAX_VE
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1483.8193
Minimum0.00929972
Maximum20926.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:15.388524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00929972
5-th percentile12.0658
Q145.1105
median49.7157
Q3732.249
95-th percentile11026.04
Maximum20926.3
Range20926.291
Interquartile range (IQR)687.1385

Descriptive statistics

Standard deviation4177.9579
Coefficient of variation (CV)2.8156784
Kurtosis12.371232
Mean1483.8193
Median Absolute Deviation (MAD)10.3922
Skewness3.5362629
Sum72707.145
Variance17455332
MonotonicityNot monotonic
2023-12-10T23:32:15.502208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
45.9861 1
 
2.0%
43.8113 1
 
2.0%
1022.24 1
 
2.0%
20926.3 1
 
2.0%
1039.31 1
 
2.0%
37.5284 1
 
2.0%
1946.34 1
 
2.0%
49.8326 1
 
2.0%
12.3591 1
 
2.0%
125.406 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.00929972 1
2.0%
8.68418 1
2.0%
11.956 1
2.0%
12.2305 1
2.0%
12.3591 1
2.0%
17.9323 1
2.0%
18.9014 1
2.0%
37.5284 1
2.0%
39.3235 1
2.0%
43.8113 1
2.0%
ValueCountFrequency (%)
20926.3 1
2.0%
15755.7 1
2.0%
11151.4 1
2.0%
10838.0 1
2.0%
2165.95 1
2.0%
1946.34 1
2.0%
1515.9 1
2.0%
1279.6 1
2.0%
1273.04 1
2.0%
1039.31 1
2.0%

NVGTN_DIST
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42971850
Minimum10227.4
Maximum91710500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:15.620328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10227.4
5-th percentile5574996
Q115880300
median38738700
Q367471900
95-th percentile85587140
Maximum91710500
Range91700273
Interquartile range (IQR)51591600

Descriptive statistics

Standard deviation28814348
Coefficient of variation (CV)0.6705401
Kurtosis-1.5438798
Mean42971850
Median Absolute Deviation (MAD)27711600
Skewness0.070597833
Sum2.1056206 × 109
Variance8.3026667 × 1014
MonotonicityNot monotonic
2023-12-10T23:32:15.742382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
91710500.0 1
 
2.0%
25712900.0 1
 
2.0%
23919500.0 1
 
2.0%
8192600.0 1
 
2.0%
11027100.0 1
 
2.0%
16578800.0 1
 
2.0%
14642800.0 1
 
2.0%
18462300.0 1
 
2.0%
15880300.0 1
 
2.0%
35656400.0 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
10227.4 1
2.0%
4243940.0 1
2.0%
5501920.0 1
2.0%
5684610.0 1
2.0%
5724130.0 1
2.0%
8192600.0 1
2.0%
8886020.0 1
2.0%
9539900.0 1
2.0%
10834200.0 1
2.0%
11027100.0 1
2.0%
ValueCountFrequency (%)
91710500.0 1
2.0%
88419700.0 1
2.0%
87920700.0 1
2.0%
82086800.0 1
2.0%
80724800.0 1
2.0%
76854000.0 1
2.0%
76479300.0 1
2.0%
75944300.0 1
2.0%
72358200.0 1
2.0%
71355800.0 1
2.0%

WAVE_MAX_CYCL
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.122738
Minimum0
Maximum23.2558
Zeros6
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:15.845466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.7527
median16.3934
Q318.8679
95-th percentile21.7391
Maximum23.2558
Range23.2558
Interquartile range (IQR)8.1152

Descriptive statistics

Standard deviation6.708252
Coefficient of variation (CV)0.47499655
Kurtosis0.069398105
Mean14.122738
Median Absolute Deviation (MAD)3.4064
Skewness-0.99826379
Sum692.01418
Variance45.000645
MonotonicityNot monotonic
2023-12-10T23:32:15.948138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0 6
 
12.2%
16.3934 5
 
10.2%
21.7391 4
 
8.2%
17.8571 4
 
8.2%
10.7527 3
 
6.1%
18.8679 2
 
4.1%
18.5185 2
 
4.1%
14.2857 2
 
4.1%
19.2308 2
 
4.1%
20.4082 2
 
4.1%
Other values (17) 17
34.7%
ValueCountFrequency (%)
0.0 6
12.2%
5.10204 1
 
2.0%
7.35294 1
 
2.0%
9.09091 1
 
2.0%
9.34579 1
 
2.0%
10.4167 1
 
2.0%
10.7527 3
6.1%
10.8696 1
 
2.0%
11.7647 1
 
2.0%
12.987 1
 
2.0%
ValueCountFrequency (%)
23.2558 1
 
2.0%
21.7391 4
8.2%
20.8333 1
 
2.0%
20.4082 2
4.1%
19.6078 1
 
2.0%
19.2308 2
4.1%
18.8679 2
4.1%
18.5185 2
4.1%
18.1818 1
 
2.0%
17.8571 4
8.2%

WAVE_AVE_CYCL
Real number (ℝ)

ZEROS 

Distinct44
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5593696
Minimum0
Maximum10.5541
Zeros6
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:16.061468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.47188
median5.02811
Q36.24818
95-th percentile9.770438
Maximum10.5541
Range10.5541
Interquartile range (IQR)4.7763

Descriptive statistics

Standard deviation3.1478129
Coefficient of variation (CV)0.69040529
Kurtosis-0.88304926
Mean4.5593696
Median Absolute Deviation (MAD)1.52752
Skewness-0.048589864
Sum223.40911
Variance9.908726
MonotonicityNot monotonic
2023-12-10T23:32:16.183927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.0 6
 
12.2%
6.26056 1
 
2.0%
0.340879 1
 
2.0%
0.862196 1
 
2.0%
4.09835 1
 
2.0%
0.0970024 1
 
2.0%
0.824248 1
 
2.0%
4.90491 1
 
2.0%
5.02811 1
 
2.0%
4.90752 1
 
2.0%
Other values (34) 34
69.4%
ValueCountFrequency (%)
0.0 6
12.2%
0.0726216 1
 
2.0%
0.0747942 1
 
2.0%
0.0970024 1
 
2.0%
0.340879 1
 
2.0%
0.824248 1
 
2.0%
0.862196 1
 
2.0%
1.47188 1
 
2.0%
1.48569 1
 
2.0%
2.99908 1
 
2.0%
ValueCountFrequency (%)
10.5541 1
2.0%
9.86347 1
2.0%
9.84497 1
2.0%
9.65864 1
2.0%
9.08759 1
2.0%
8.86796 1
2.0%
8.66073 1
2.0%
8.11905 1
2.0%
7.91349 1
2.0%
6.55563 1
2.0%

WAVE_MAX_HGHT
Real number (ℝ)

ZEROS 

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5481633
Minimum0
Maximum8.88
Zeros6
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:16.305473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.95
median3.31
Q34.92
95-th percentile7.164
Maximum8.88
Range8.88
Interquartile range (IQR)2.97

Descriptive statistics

Standard deviation2.2124832
Coefficient of variation (CV)0.62355733
Kurtosis-0.43935611
Mean3.5481633
Median Absolute Deviation (MAD)1.43
Skewness0.24236085
Sum173.86
Variance4.895082
MonotonicityNot monotonic
2023-12-10T23:32:16.409998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 6
 
12.2%
1.93 3
 
6.1%
2.09 3
 
6.1%
3.03 2
 
4.1%
5.12 2
 
4.1%
3.53 2
 
4.1%
5.71 2
 
4.1%
3.24 2
 
4.1%
6.96 1
 
2.0%
4.9 1
 
2.0%
Other values (25) 25
51.0%
ValueCountFrequency (%)
0.0 6
12.2%
1.3 1
 
2.0%
1.48 1
 
2.0%
1.88 1
 
2.0%
1.93 3
6.1%
1.95 1
 
2.0%
2.09 3
6.1%
2.49 1
 
2.0%
2.53 1
 
2.0%
2.6 1
 
2.0%
ValueCountFrequency (%)
8.88 1
2.0%
7.33 1
2.0%
7.3 1
2.0%
6.96 1
2.0%
6.91 1
2.0%
6.73 1
2.0%
6.71 1
2.0%
5.71 2
4.1%
5.12 2
4.1%
5.01 1
2.0%

WAVE_AVE_HGHT
Real number (ℝ)

ZEROS 

Distinct44
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74325945
Minimum0
Maximum1.60245
Zeros6
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:16.524748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.206375
median0.899801
Q31.15219
95-th percentile1.377602
Maximum1.60245
Range1.60245
Interquartile range (IQR)0.945815

Descriptive statistics

Standard deviation0.50474406
Coefficient of variation (CV)0.67909538
Kurtosis-1.2719463
Mean0.74325945
Median Absolute Deviation (MAD)0.348801
Skewness-0.30679486
Sum36.419713
Variance0.25476656
MonotonicityNot monotonic
2023-12-10T23:32:16.644371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.0 6
 
12.2%
1.20871 1
 
2.0%
0.04 1
 
2.0%
0.212824 1
 
2.0%
0.4825 1
 
2.0%
0.0111243 1
 
2.0%
0.10375 1
 
2.0%
0.799706 1
 
2.0%
0.833444 1
 
2.0%
0.810745 1
 
2.0%
Other values (34) 34
69.4%
ValueCountFrequency (%)
0.0 6
12.2%
0.00774869 1
 
2.0%
0.0111243 1
 
2.0%
0.012037 1
 
2.0%
0.04 1
 
2.0%
0.10375 1
 
2.0%
0.177115 1
 
2.0%
0.206375 1
 
2.0%
0.212824 1
 
2.0%
0.407143 1
 
2.0%
ValueCountFrequency (%)
1.60245 1
2.0%
1.52619 1
2.0%
1.39137 1
2.0%
1.35695 1
2.0%
1.34366 1
2.0%
1.31871 1
2.0%
1.28357 1
2.0%
1.25489 1
2.0%
1.22761 1
2.0%
1.20871 1
2.0%

MAX_WDSP
Real number (ℝ)

ZEROS 

Distinct44
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.104181
Minimum0
Maximum27.0893
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:16.768226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.287776
Q112.6314
median15.7171
Q318.0874
95-th percentile20.60642
Maximum27.0893
Range27.0893
Interquartile range (IQR)5.456

Descriptive statistics

Standard deviation4.7184488
Coefficient of variation (CV)0.31239356
Kurtosis1.5383781
Mean15.104181
Median Absolute Deviation (MAD)3.0857
Skewness-0.59010591
Sum740.10487
Variance22.26376
MonotonicityNot monotonic
2023-12-10T23:32:16.918544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
19.4686 4
 
8.2%
12.6314 2
 
4.1%
16.5972 2
 
4.1%
0.0 1
 
2.0%
16.5703 1
 
2.0%
9.3048 1
 
2.0%
13.0485 1
 
2.0%
8.62883 1
 
2.0%
14.8374 1
 
2.0%
12.7568 1
 
2.0%
Other values (34) 34
69.4%
ValueCountFrequency (%)
0.0 1
2.0%
5.72332 1
2.0%
6.39374 1
2.0%
8.62883 1
2.0%
9.3048 1
2.0%
9.67128 1
2.0%
10.04 1
2.0%
10.2115 1
2.0%
11.0999 1
2.0%
11.9335 1
2.0%
ValueCountFrequency (%)
27.0893 1
 
2.0%
22.5688 1
 
2.0%
21.0117 1
 
2.0%
19.9985 1
 
2.0%
19.4686 4
8.2%
19.4205 1
 
2.0%
19.131 1
 
2.0%
18.825 1
 
2.0%
18.2409 1
 
2.0%
18.0874 1
 
2.0%

AVE_WDSP
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4897059
Minimum0
Maximum6.39261
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:17.074771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.323774
Q13.74104
median4.6491
Q35.55632
95-th percentile6.260822
Maximum6.39261
Range6.39261
Interquartile range (IQR)1.81528

Descriptive statistics

Standard deviation1.3256893
Coefficient of variation (CV)0.29527307
Kurtosis1.5885246
Mean4.4897059
Median Absolute Deviation (MAD)0.90806
Skewness-1.0422547
Sum219.99559
Variance1.757452
MonotonicityNot monotonic
2023-12-10T23:32:17.216763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0 1
 
2.0%
4.49586 1
 
2.0%
5.73516 1
 
2.0%
5.54323 1
 
2.0%
4.27358 1
 
2.0%
5.04321 1
 
2.0%
4.40792 1
 
2.0%
4.76373 1
 
2.0%
3.47528 1
 
2.0%
4.228 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0 1
2.0%
1.51568 1
2.0%
2.07017 1
2.0%
2.70418 1
2.0%
2.71231 1
2.0%
2.73427 1
2.0%
3.24415 1
2.0%
3.28549 1
2.0%
3.44747 1
2.0%
3.47528 1
2.0%
ValueCountFrequency (%)
6.39261 1
2.0%
6.36575 1
2.0%
6.28139 1
2.0%
6.22997 1
2.0%
5.88435 1
2.0%
5.80433 1
2.0%
5.80236 1
2.0%
5.73516 1
2.0%
5.72914 1
2.0%
5.69054 1
2.0%

LD_RT
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11129475
Minimum0.00209658
Maximum0.960148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:17.362527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00209658
5-th percentile0.003920436
Q10.014877
median0.0289729
Q30.177716
95-th percentile0.3569982
Maximum0.960148
Range0.95805142
Interquartile range (IQR)0.162839

Descriptive statistics

Standard deviation0.17048783
Coefficient of variation (CV)1.5318587
Kurtosis11.960543
Mean0.11129475
Median Absolute Deviation (MAD)0.02398715
Skewness2.9588246
Sum5.4534427
Variance0.0290661
MonotonicityNot monotonic
2023-12-10T23:32:17.490923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.413845 1
 
2.0%
0.0222283 1
 
2.0%
0.00435415 1
 
2.0%
0.0108327 1
 
2.0%
0.233919 1
 
2.0%
0.0128868 1
 
2.0%
0.0036719 1
 
2.0%
0.0228418 1
 
2.0%
0.0400409 1
 
2.0%
0.0283445 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.00209658 1
2.0%
0.00230882 1
2.0%
0.0036719 1
2.0%
0.00429324 1
2.0%
0.00435415 1
2.0%
0.00498575 1
2.0%
0.00581738 1
2.0%
0.0108327 1
2.0%
0.0111155 1
2.0%
0.0127377 1
2.0%
ValueCountFrequency (%)
0.960148 1
2.0%
0.413845 1
2.0%
0.368887 1
2.0%
0.339165 1
2.0%
0.298865 1
2.0%
0.294665 1
2.0%
0.283126 1
2.0%
0.282377 1
2.0%
0.270471 1
2.0%
0.243942 1
2.0%

RN
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:17.616342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.4
Q114
median26
Q338
95-th percentile47.6
Maximum50
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.54956501
Kurtosis-1.2
Mean26
Median Absolute Deviation (MAD)12
Skewness0
Sum1274
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:32:17.744431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
11 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

Sample

MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOAVE_VEMAX_VENVGTN_DISTWAVE_MAX_CYCLWAVE_AVE_CYCLWAVE_MAX_HGHTWAVE_AVE_HGHTMAX_WDSPAVE_WDSPLD_RTRN
02056810009659139WARISOULXLPG Tanker30.0165.018.2<NA>102<NA>20152859001-Jan-2021 00:01:0131-Jul-2021 23:56:47-3.58239-81.68389927.8305-97.49749814.850345.986191710500.00.00.00.00.00.00.00.4138452
12056850009687497KNOKKELPG Tanker0.00.00.0<NA>0<NA>20162947801-Jan-2021 00:00:5931-Jul-2021 23:55:5051.3399013.7655560.8153995.042613.666949.257951668000.019.23086.152887.331.0100927.08934.852940.1314173
22056860009709087ALICECrude Oil Tanker0.00.00.0<NA>0<NA>201629932001-Jan-2021 00:18:1731-Jul-2021 23:57:2214.9983-106.713997-9.24461100.52811.521849.471869790500.021.73919.658644.881.5261915.18384.64910.2831264
32056910009687502KONTICHLPG Tanker0.00.00.0<NA>0<NA>20162946101-Jan-2021 00:04:5631-Jul-2021 23:56:2345.385899-8.6195259.27055.4967720.334549.743871355800.018.18185.204488.880.94411518.08745.884350.0314365
42056920009722924ALEXCrude Oil Tanker0.00.00.0<NA>0<NA>201629944601-Jan-2021 03:17:0631-Jul-2021 07:59:1320.981899112.50099935.099998119.41100312.087549.964665392700.019.60788.867965.121.3436616.74024.5640.1777166
52057001119255763<NA><NA>0.00.00.0<NA>0<NA>0027-Jul-2021 01:07:0328-Jul-2021 00:20:2735.11069929.21620.00.09802.1610838.05684610.00.00.00.00.06.393745.288180.9601487
62057030009750024ENGIE ZEEBRUGGELNG Tanker0.00.00.0<NA>0<NA>2017312101-Jan-2021 00:00:2431-Jul-2021 23:56:000.00.052.1685984.2220730.021949.522810834200.09.090915.267473.371.001219.46865.804330.0111168
72057060009687514KORTRIJKLPG Tanker0.00.00.0<NA>0<NA>20162958801-Jan-2021 00:15:4931-Jul-2021 23:53:0739.8083-75.41336.3452.4266714.342845.110588419700.018.86791.485692.530.17711518.24091.515680.3688879
82057090009732577AQUITAINECrude Oil Tanker0.00.00.0<NA>0<NA>201729876701-Jan-2021 00:00:0531-Jul-2021 23:57:23-25.94370113.7260.00.0282.8211273.0460894400.019.23089.863474.281.3187114.2245.113290.01273810
92057100009732565ARDECHECrude Oil Tanker0.00.00.0<NA>0<NA>201729864201-Jan-2021 00:41:4831-Jul-2021 23:58:00-12.98668.789703-4.8780511.005412.053349.867675944300.018.51859.087596.911.3913716.93824.002350.19336411
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOAVE_VEMAX_VENVGTN_DISTWAVE_MAX_CYCLWAVE_AVE_CYCLWAVE_MAX_HGHTWAVE_AVE_HGHTMAX_WDSPAVE_WDSPLD_RTRN
392115137900BIBERACHInland Unknown10.085.00.0<NA>0<NA>0001-Jan-2021 00:04:1531-Jul-2021 04:59:3651.2406014.416470.00.0220.7492165.9562548700.012.9875.058433.040.91017511.09994.066740.06690541
402115140400VERANOInland Motor Tanker liquid cargo type C11.0110.00.0<NA>0<NA>0001-Jan-2021 00:33:2331-Jul-2021 23:56:180.00.00.00.0150.597811.57737029500.010.75274.964183.030.85897819.46863.795070.02897342
412115143400MARIONInland Motor Tanker liquid cargo type C11.0106.00.0<NA>0<NA>1986165501-Jan-2021 00:00:1430-Jul-2021 14:25:1948.13600217.146547.88349917.540122.713449.365326514700.015.8735.42973.531.0494312.21693.62670.01903643
422115144200TANJA DEYMANN 2Inland Motor Tanker liquid cargo type C9.680.50.0<NA>0<NA>0004-Jan-2021 04:28:2331-Jul-2021 09:15:0952.19490111.684953.297110.488835.623449.715767042000.017.85715.570223.531.0870710.21153.520840.07886844
432071330006817857AGAMEMNONOil Products Tanker9.8664.010.0<NA>0<NA>1968106101-Jan-2021 00:00:0231-Jul-2021 23:55:0743.18920127.91443.18920127.91396.431638.684184243940.00.00.00.00.016.59725.037310.01363945
442072280009010931GALAXY ECOBunkering Tanker14.090.00.0<NA>0<NA>1993269801-Jan-2021 00:01:0431-Jul-2021 23:58:1243.19390127.9066010.00.010807.411151.45724130.00.00.00.00.016.59725.124390.00230946
452072610120MAX PLANCKInland Motor Tanker liquid cargo type C11.4110.00.0<NA>0<NA>1992247706-Jan-2021 06:12:1631-Jul-2021 23:56:0444.32389828.036944.14820128.660221.166249.973229557300.013.33331.471883.310.20637510.044.579380.02690147
4620726120847000004GEORGI IZMIRLIEVInland Pushtow six cargo barges34.0209.00.0<NA>0<NA>0001-Jan-2021 00:00:0231-Jul-2021 23:56:3744.11510128.6430.00.098.2626307.84420402400.011.76470.0726221.950.01203712.08545.372830.00498648
472072614140MADARAInland Pushtow two barges at least one tanker22.0111.00.0<NA>0<NA>0004-Jan-2021 13:03:4725-Jul-2021 20:24:0044.27959828.07850143.85720125.9548327.062732.24913150100.014.28570.0747941.480.00774912.63145.566170.00429349
482072617100YAKOSInland Motor Tanker liquid cargo type N9.595.30.0<NA>0<NA>0005-Jan-2021 13:44:2825-Jul-2021 19:32:4343.83660125.9311010.00.031.74944.454816534200.016.39344.45681.930.5515.723322.712310.02326650